Identify-Customer-Segments

Udacity Data Scientist Nanodegree Project 3 Identify Customer Segments

Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

Installation

There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.

Project Motivation

This is the third project of Udacity Data Scientist Nanodegree. In this project, I applied unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns.

File Descriptions

There is one iPython notebook to showcase work related to this project. The html file was generated from iPython notebook to submit this project.

Results

Cluster 4 is overrepresented in the customers data compared to general population data. Some characteristics of the group of population that are relative popular with the mail-order company:

• in areas where the share of 6-10 family homes is lower (PLZ8_ANTG3=1.73)
• in Prosperous or Comfortable households (WEALTH=2.75)
• in life stage of Families With School Age Children or Older Families & Mature Couples (LIFE_STAGE=3.30)

Cluster 13 is underrepresented in the customers data. Some characteristics of the segment of the population that are relatively unpopular with the company:

• in areas where the share of 6-10 family homes is higher (PLZ8_ANTG3=2.44)
• in Less Affluent or Poorer households (WEALTH=4.4)
• in life stage of Pre-Family Couples & Singles or Young Couples With Children (LIFE_STAGE=1.98)

Licensing, Authors, Acknowledgements

Credits must be given to Udacity for providing starting code for this project. The data was provided by Udacity partners at Bertelsmann Arvato Analytics.